Subt-MRS Dataset

• This project was an year-long endeavor, where I co-led a team of 5 in acquiring a 2 TB SubT-MRS SLAM dataset, collected on 3 UGVs and 1 Boston Dynamics Spot, 5 sensors including RGB camera, thermal camera, IMU, and LIDAR, and 5 locations with different characteristics
• As a part of data analysis, I implemented and evaluated 4 state-of-the-art SLAM algorithms, LIO-SAM, LOAM, FAST-LIO, and CLINS on the dataset to gauge the difficulty of the dataset
• I also designed multiple Python3 scripts for dataset cleaning and statistics generation. I also implemented a PyTorch DataLoader for the dataset and integrated it into a pip package
• I integrated real-time point cloud colorization into the SuperOdometry algorithm by field-of-view analysis between RGB camera and LIDAR for improving visualizations
• This dataset will be used to host the ICCV 2023 Workshop on Robot Learning and SLAM